热邻近共聚集方法的计算优化及其在分析细胞内蛋白质复合物动态变化中的应用

蛋白-蛋白相互作用是生物化学及化学生物学研究中的重要课题之一，而蛋白质复合物作为蛋白质网络的核心，是蛋白质网络发挥生物活性的关键。为了更全面地了解蛋白质复合物的时空特性，热邻近共聚集方法（Thermal Proximity Co-Aggregation, TPCA）于2018年提出，该方法可以在保证细胞完整生命活动的同时，在蛋白质组学层面上分析时间和空间特异的蛋白-蛋白相互作用的动态变化。TPCA方法在过去的几年中得到了广泛的认可及应用，但该方法仍存在较大的优化空间。

为了提高该方法的分析能力，本研究改进了TPCA方法中的计算分析步骤，优化了数据处理和分析工作的流程，并将优化后的方法命名为Slim-TPCA。Slim-TPCA方法从实验设计和数据分析角度提出了一系列改进措施，不仅简化了实验设计，还能消除不同批次质谱数据之间的批次效应，缩短统计计算所需时间，提高了鉴定蛋白质复合物动态变化的灵敏度。Slim-TPCA方法被证明可以用于研究葡萄糖蛋白质组学数据中的蛋白-蛋白相互作用变化，并在研究相分离蛋白的领域展露了潜力。

此外，依托本课题组开发的ProSAP软件，本研究对以往的蛋白质数据库进行了整理，构建了Consensus复合物数据库。该数据库提供了更全面的蛋白质复合物信息，用户在使用ProSAP软件分析Slim-TPCA数据时可以选择该数据库进行更全面的分析。最后，本研究还探究了不同种类去污剂对CETSA和Slim-TPCA方法的影响，证明去污剂有助于CETSA方法和Slim-TPCA方法研究蛋白-配体结合和蛋白-蛋白相互作用。

Protein-protein interactions are one of the most important topics in biochemistry and chemical biology research, and protein complexes, as cores of the protein network, are the keys to the biological activity of protein networks. To gain a more comprehensive understanding of the spatial and temporal properties of protein complexes, the Thermal Proximity Co-Aggregation (TPCA) method was proposed in 2018, which can analyze the time- and space-specific protein-protein interaction variations at the proteomic level while ensuring the vital activities of cells. The TPCA method has been widely recognized and applied in the past few years, but there is still room for optimization.

In this thesis, I mainly work on the computational analysis and optimization of the TPCA method, cumulating to optimized data processing and analytical workflow named Slim-TPCA. The Slim-TPCA method offers a series of improvements in terms of experimental design and data analysis, which simplify the experimental design, eliminate the batch effect between different batches of mass spectrometry analysis, shorten the time required for statistical calculations, and improve the sensitivity of identifying variations of protein complexes. The Slim-TPCA method was demonstrated to be effective for studying protein-protein interactions in glycol-proteomic data and has shown potential in the field of studying phase-separated proteins.

In addition, relying on the ProSAP software developed by our group, I organize the previous protein complex databases and propose the Consensus database, which provides more comprehensive information on protein complexes. Finally, I also explore the effects of different detergents on the CETSA and Slim-TPCA methods and demonstrate that detergents help the CETSA method and Slim-TPCA method to study protein-ligand binding and protein-protein interactions.

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